#!/bin/bash


#集合通信参数,不需要修改
export RANK_SIZE=1

# 数据集路径,保持为空,不需要修改
data_path=""
# 训练周期数
epochs=1
#规避环境变量冲突
if [ -f /usr/local/Ascend/bin/setenv.bash ];then
    unset PYTHONPATH
    source /usr/local/Ascend/bin/setenv.bash  
fi
#网络名称,同目录名称,需要模型审视修改
Network="DenseNet169_ID0454_for_PyTorch"

#训练batch_size,,需要模型审视修改
batch_size=128

# 指定训练所使用的npu device卡id
device_id=0
# 参数校验，data_path为必传参数，其他参数的增删由模型自身决定；此处新增参数需在上面有定义并赋值
for para in $*
do
    if [[ $para == --device_id* ]];then
        device_id=`echo ${para#*=}`
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
    elif [[ $para == --epochs* ]];then
        epochs=`echo ${para#*=}`
    fi
done

#校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1
fi
# 校验是否指定了device_id,分动态分配device_id与手动指定device_id,此处不需要修改
if [ $ASCEND_DEVICE_ID ];then
    echo "device id is ${ASCEND_DEVICE_ID}"
elif [ ${device_id} ];then
    export ASCEND_DEVICE_ID=${device_id}
    echo "device id is ${ASCEND_DEVICE_ID}"
else
    "[Error] device id must be config"
    exit 1
fi
###############指定训练脚本执行路径###############
# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
cur_path=`pwd`
cur_path_last_dirname=${cur_path##*/}
if [ x"${cur_path_last_dirname}" == x"test" ];then
    test_path_dir=${cur_path}
    cd ..
    cur_path=`pwd`
else
    test_path_dir=${cur_path}/test
fi
#进入训练脚本目录，需要模型审视修改
cd $cur_path/references/classification

#创建DeviceID输出目录，不需要修改
if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
    rm -rf ${test_path_dir}/output
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
else
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
fi


#################启动训练脚本#################
#训练开始时间，不需要修改
start_time=$(date +%s)
# 非平台场景时source 环境变量
check_etp_flag=`env | grep etp_running_flag`
etp_flag=`echo ${check_etp_flag#*=}`
if [ x"${etp_flag}" != x"true" ];then
    source ${test_path_dir}/env_npu.sh
    nohup taskset -c 0-23 python3 train.py  \
        --model densenet169 \
        --epochs ${epochs} \
        --data-path=$data_path \
        --batch-size=$batch_size \
        --workers 16 \
        --lr 0.1 \
        --momentum 0.9 \
        --weight-decay 1e-4 \
        --apex \
        --apex-opt-level O2 \
        --loss_scale_value 1024 \
        --seed 1234 \
        --device_id=$ASCEND_DEVICE_ID \
        --print-freq 10 > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
else
    nohup python3 train_mp.py  \
        --model densenet169 \
        --epochs ${epochs} \
        --data-path=$data_path \
        --batch-size=$batch_size \
        --workers 16 \
        --lr 0.1 \
        --momentum 0.9 \
        --weight-decay 1e-4 \
        --apex \
        --apex-opt-level O2 \
        --loss_scale_value 1024 \
        --seed 1234 \
        --device_id=$ASCEND_DEVICE_ID \
        --print-freq 10 > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
fi
wait

##################获取训练数据################
#训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

#结果打印，不需要修改
echo "------------------ Final result ------------------"
#输出性能FPS，需要模型审视修改
FPS=`grep 'Epoch:'  ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|tail -n+2|awk -F "img/s: " '{print $2}'|awk '{sum+=$1} END {print sum/(NR-1)}'`
#打印，不需要修改
echo "Final Performance images/sec : $FPS"

#获取编译时间
CompileTime=`grep "Epoch:" ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | head -1 | awk -F "time:" '{print $2}' | awk -F " " '{print $1}' | awk '{sum+=$1} END {print"",sum}' |sed s/[[:space:]]//g`

#打印，不需要修改
echo "E2E Training Duration sec : $e2e_time"

#性能看护结果汇总
#训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'

#获取性能数据，不需要修改
#吞吐量
ActualFPS=${FPS}
#单迭代训练时长
TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'*1000/'${FPS}'}'`

#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要模型审视修改
grep Epoch: ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|grep eta:|awk -F "loss: " '{print $NF}' | awk -F " " '{print $1}' >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt

#最后一个迭代loss值，不需要修改
ActualLoss=`awk 'END {print}' ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

#关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" > ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualFPS = ${ActualFPS}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainingTime = ${TrainingTime}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CompileTime = ${CompileTime}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log